Adaptive control of data collection requests sent to external data sources

ABSTRACT

Techniques and mechanisms are disclosed that enable a data collection system to adaptively control collection of data from one or more external data sources. At a high level, adaptively controlling collection of data from external data sources may include collecting performance information related to one or more data collection nodes and, in response to analyzing the collected performance information, adapting rates at which the data collection nodes send data collection requests to external data sources. Data collection performance information generally may include, but is not limited to, network traffic data, error messages generated by external data sources and/or data collection nodes, computing device performance information, and any other types of information related to a data collection node&#39;s ability to collect data from external data sources.

CROSS-REFERENCE TO RELATED APPLICATIONS; PRIORITY CLAIM

This application claims the benefit under 35 U.S.C. §120 as aContinuation-in-part of U.S. application Ser. No. 14/902,848, filed Jan.4, 2016, which is a U.S. National Stage patent application filed under35 U.S.C. §371 of International Application No. PCT/CN15/90177, filedSep. 21, 2015, the entire contents of all of which are herebyincorporated by reference as if fully set forth herein.

TECHNICAL FIELD

Embodiments relate generally to techniques for generating graphicalvisualizations and other displays of event data related to collectionsof computing resources.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

An increasingly large number of organizations rely on various types ofcomputing resources provided by cloud computing service providers aspart of their computing infrastructure. A cloud computing servicegenerally comprises a collection of remote computing services (e.g.,servers, storage, networking, backup, etc.) made available to usersbased on various payment models. Examples of popular cloud computingservices include Amazon Web Services (AWS) offered by Amazon.com, Azureoffered by Microsoft, and Google Cloud Platform offered by Google.

Cloud computing services typically provide a web-based managementconsole or other interface that enables users to manage their cloudcomputing resources. For example, a typical management console mayprovide one interface that displays a list of a user's active cloudserver instances, another interface that displays a list of storagevolumes associated with the server instances, yet another interface thatdisplays a list of a user's configured virtual private clouds, and soforth. While such interfaces may be useful for viewing informationrelated to some aspects of an organization's cloud computing resources,the disconnected nature of such interfaces presents challenges toreadily obtaining a broader picture of the cloud computing resources andrelationships among those resources.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12-14 illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIGS. 15-17 illustrate example visualizations generated by a reportingapplication in accordance with the disclosed embodiments;

FIG. 18 illustrates an overview of a networked computer environment thatenables a cloud computing management application to collect data relatedto a collection of cloud computing resources and to display variousvisualizations related to the cloud computing resources in accordancewith the disclosed embodiments;

FIG. 19 illustrates a cloud computing key indicators view providinginformation related to a collection of cloud computing resources inaccordance with the disclosed embodiments;

FIG. 20 illustrates an interface including a topology map representing acollection of cloud computing resources and generated based on datacollected from one or more cloud computing services and/or other datasources in accordance with the disclosed embodiments;

FIG. 21 illustrates a user interface screen that enables a user toconfigure one or more data sources for collection of data related to oneor more cloud computing resources in accordance with the disclosedembodiments;

FIG. 22 illustrates an interface including display of informationrelated to a selected node of a displayed topology map in accordancewith the disclosed embodiments;

FIG. 23 illustrates an interface screen displaying a panel providingdetailed information related to a selected map element in accordancewith the disclosed embodiments;

FIG. 24 illustrates a portion of an example interface which enablesusers to associate notes and/or tags with one or more elements of atopology map in accordance with the disclosed embodiments;

FIG. 25 illustrates a portion of an example interface for specifying oneor more triggers to associate with one or more selected elements of atopology map in accordance with the disclosed embodiments;

FIG. 26 illustrates a portion of an example interface for selecting oneor more topology map elements and providing input to export data relatedto the selected elements in accordance with the disclosed embodiments;

FIG. 27 illustrates a portion of an example interface that enables usersto select one or more topology map elements and provide further input togenerate another types of data visualizations based on the selectedelements in accordance with the disclosed embodiments;

FIG. 28 illustrates a portion of an example interface for displaying atime-lapse of a topology map in accordance with the disclosedembodiments;

FIGS. 29A-29B illustrate a portion of an example interface displaying atopology map in synchronization with a separate data visualization inaccordance with the disclosed embodiments;

FIG. 30 illustrates a portion of an example interface displaying atopology map indicating differences between states of the topology mapat two different points in time in accordance with the disclosedembodiments;

FIG. 31 is a flow diagram that illustrates generation of a graphicaluser interface displaying a topology map in accordance with thedisclosed embodiments;

FIG. 32 is a flow diagram that illustrates generation of a graphicaluser interface displaying an animated topology map in accordance withthe disclosed embodiments;

FIG. 33 illustrates a block diagram of data collection nodes and a datacollection controller node configured to collect data from one or moreexternal data sources in accordance with the disclosed embodiments;

FIG. 34 is a flow diagram that illustrates a process for adaptivelycontrolling data collection requests sent to external data sources inaccordance with the disclosed embodiments;

FIG. 35 illustrates a computer system upon which an embodiment may beimplemented.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

Embodiments are described herein according to the following outline:

-   -   1.0. General Overview    -   2.0. Operating Environment    -   2.1. Host Devices    -   2.2. Client Devices    -   2.3. Client Device Applications    -   2.4. Data Server System    -   2.5. Data Ingestion        -   2.5.1. Input        -   2.5.2. Parsing        -   2.5.3. Indexing    -   2.6. Query Processing    -   2.7. Field Extraction    -   2.8. Example Search Screen    -   2.9. Data Modelling    -   2.10. Acceleration Techniques        -   2.10.1. Aggregation Technique        -   2.10.2. Keyword Index        -   2.10.3. High Performance Analytics Store        -   2.10.4. Accelerating Report Generation    -   2.11. Security Features    -   2.12. Data Center Monitoring    -   2.13. Cloud-Based System Overview    -   2.14. Searching Externally Archived Data    -   2.14.1. ERP Process Features    -   3.0. Functional Overview    -   3.1. Cloud Computing Management Application Overview    -   3.2 Cloud Computing Resource Data Collection        -   3.2.1. Configuring Resource Data Collection        -   3.2.2. Data Collection Process        -   3.2.3. Adaptive Data Collection Process    -   3.3. Cloud Computing Resource Topology Maps        -   3.3.1. Generating Topology Map Displays        -   3.3.2. Interacting With Topology Map Displays        -   3.3.3. Analyzing Displayed Topology Maps        -   3.3.4. Displaying Time-Based Topology Maps    -   4.0. Implementation Examples    -   4.1. Generating Cloud Computing Resource Topology Map Displays    -   4.2. Generating Topology Map Time-lapse Displays    -   5.0. Example Embodiments    -   6.0. Implementation Mechanism—Hardware Overview    -   7.0. Extensions and Alternatives

1.0. GENERAL OVERVIEW

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. OPERATING ENVIRONMENT

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 12 illustrates an example interactive data modelselection graphical user interface 1200 of a report editor that displaysa listing of available data models 1201. The user may select one of thedata models 1202.

FIG. 13 illustrates an example data model object selection graphicaluser interface 1300 that displays available data objects 1301 for theselected data object model 1202. The user may select one of thedisplayed data model objects 1302 for use in driving the reportgeneration process.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7A may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., label the column to bedisplayed as “component”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 14 illustrates an example graphical user interface screen 1400 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1401 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1402. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1406. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1403. A count of the number of successful purchases foreach product is displayed in column 1404. These statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1405, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 15 illustrates an example graphical user interface 1500 thatdisplays a set of components and associated statistics 1501. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.16 illustrates an example of a bar chart visualization 1600 of an aspectof the statistical data 1501. FIG. 17 illustrates a scatter plotvisualization 1700 of an aspect of the statistical data 1501.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 806 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developer's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.14. It Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE facilitates the production of meaningfulaggregate KPI's through a system of KPI thresholds and state values.Different KPI definitions may produce values in different ranges, and sothe same value may mean something very different from one KPI definitionto another. To address this, SPLUNK® IT SERVICE INTELLIGENCE implementsa translation of individual KPI values to a common domain of “state”values. For example, a KPI range of values may be 1-100, or 50-275,while values in the state domain may be ‘critical,’ ‘warning,’ ‘normal,’and ‘informational’. Thresholds associated with a particular KPIdefinition determine ranges of values for that KPI that correspond tothe various state values. In one case, KPI values 95-100 may be set tocorrespond to ‘critical’ in the state domain. KPI values from disparateKPI's can be processed uniformly once they are translated into thecommon state values using the thresholds. For example, “normal 80% ofthe time” can be applied across various KPI's. To provide meaningfulaggregate KPI's, a weighting value can be assigned to each KPI so thatits influence on the calculated aggregate KPI value is increased ordecreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICE INTELLIGENCEcan also be created and updated by an import of tabular data (asrepresented in a CSV, another delimited file, or a search query resultset). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE provides various visualizations built onits service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.0. FUNCTIONAL OVERVIEW

Approaches, techniques, and mechanisms are disclosed that enablecollection of various types of data from cloud computing services andthe generation of various dashboards and visualizations to viewinformation about collections of cloud computing resources. In oneembodiment, a user can configure collection of data from one or morecloud computing services and view visualizations using an applicationplatform referred to herein as a cloud computing management application.For example, a cloud computing management application may be implementedas an “app” or “add-on” of a data intake and query system, such as theSPLUNK® ENTERPRISE platform. In other implementations, a cloud computingmanagement application may be part of another type of application orimplemented as a stand-alone application.

As used herein, a cloud computing service generally refers to anycollection of remote computing services offered by a cloud computingservice provider. Non-limiting examples of cloud computing servicesinclude Amazon Web Services (AWS®) offered by Amazon.com, Inc. ofSeattle, Wash., Microsoft AZURE™ offered by Microsoft Corporation ofRedmond, Wash., and Google Cloud Platform™ offered by Google, Inc. ofMountain View, Calif. In the context of such cloud computing services,cloud computing resources generally refer to any instances and/orassociated components of the services offered by a cloud computingservice provider. Examples of cloud computing resources include, but arenot limited to, server instances, storage volumes, virtual privateclouds, subnets, access control lists, etc.

A user or organization may purchase cloud computing resources from oneor more cloud computing services for any of a vast number of purposes,including to host and execute web-based applications developed by theorganization, to provide data storage for the organization's data, toprovide computing resources to perform data analysis, etc. For example,an organization developing a web-based mobile application may pay to usea number of cloud-based server instances to host and execute applicationcode, purchase storage volumes and database servers to store and processapplication data, and so forth, typically on a “pay as you go” orsimilar payment model. By using computing resources provided by a cloudcomputing service provider, an organization can avoid some of theupfront investments in hardware and maintenance costs that may otherwisebe incurred if the organization purchased computing hardware for itself.Furthermore, scaling an organization's computing resource needs oftenmay be more easily accomplished using a cloud-based computing service asan organization's compute and/or storage needs increase or decrease.

Cloud computing service providers typically provide web-based managementconsoles that enable users of the services to create, modify, delete,and view cloud computing resources provisioned by the users. Forexample, a cloud computing service may provide one interface thatdisplays a list of a user's currently deployed server instances andfurther enables the user to create new server instances, modify variousaspects of specific server instances, and delete server instances. Manycloud computing service providers further may group resources intovarious geographic regions (e.g., to reduce network latency and improvefault-tolerance), and a user may also be able to use the managementconsole interfaces to select particular regions and view serverinstances associated with each region. Another example interface may beprovided that displays a list of storage volumes currently in use,including references to one or more server instances associated witheach of the storage volumes. Yet another separate interface may beprovided that displays a list of configured virtual private clouds, andso forth.

While conventional cloud computing service management consoles enableusers to view some information related to the user's cloud computingresources, the disconnected nature of such interfaces may presentchallenges when attempting to obtain broader information about an entirecollection of resources. For example, if a user is investigating theoperation of a particular server instance, the user may view onemanagement console interface for information about the server instanceitself, then navigate to a separate interface to view information aboutone or more storage volumes connected to the server instance, and thennavigate to yet another interface to view information about a virtualprivate cloud with which the server instance is associated, etc. Thenavigation across such interfaces may involve manual cross-correlationof resource identifiers or other information to locate the appropriateresources. Furthermore, if the server instances of interest are spreadacross different service regions, across separate user accounts, or evenacross different cloud service providers, the investigation and analysisprocess becomes increasingly cumbersome.

In one embodiment, a cloud computing management application isconfigured to collect data related to a collection of cloud computingresources and to store the data in a format that enables more efficientanalysis, for example, as event data stored by a data intake and querysystem. The collected data generally may comprise any availableinformation related to the computing resources, including performancedata, relationship data, state data, log data, etc., and may originatefrom cloud computing service providers or any other source. In anembodiment, a cloud computing management application is furtherconfigured to generate and cause display of interactive topology maprepresentations of cloud computing resources based on the collecteddata. As described hereinafter, an interactive topology map enablesusers to view an intuitive visualization of a collection of computingresources, efficiently cause performance of actions with respect tovarious resources displayed in the topology map, and analyze thecollection of resources in ways that are not possible using conventionalcloud computing service management consoles.

In other aspects, the invention encompasses a computer apparatus and acomputer-readable medium configured to carry out the foregoing steps.

3.1. Cloud Computing Management Application Overview

FIG. 18 illustrates an overview of a networked computer environment thatenables a cloud computing management application to collect data relatedto a collection of cloud computing resources and to display variousvisualizations related to the cloud computing resources. The networkedcomputer environment of FIG. 18 includes cloud computing services 1802Aand 1802B and data intake and query system 108.

In an embodiment, each of cloud computing services 1802A and 1802Bgenerally represents any collection of remote computing services offeredby a cloud computing service provider. As described above, non-limitingexamples of cloud computing services include Amazon Web Services (AWS®)offered by Amazon.com, Inc. of Seattle, Wash., Microsoft AZURE™ offeredby Microsoft Corporation of Redmond, Wash., and Google Cloud Platform™offered by Google, Inc. of Mountain View, Calif. For illustrativepurposes, two separate cloud computing services are depicted in FIG. 18,however, a cloud computing management application as described hereinmay collect data from any number of separate cloud computing services.

In an embodiment, each of cloud computing services 1802A, 1802B includesa collection of computing resources 1804A, 1804B, performance data1806A, 1806B, and relationship data 1808A, 1808B, respectively. Thecomputing resources 1804A, 1804B generally represent any instancesand/or associated components of one or more types of computing resourcesoffered by the respective cloud computing services. For example, usersgenerally can create user accounts with one or more cloud computingservices and create server instances, storage volumes, virtual privateclouds, etc.

In an embodiment, performance data 1806A, 1806B and relationship data1808A, 1808B generally represent various types of data generated by therespective cloud computing services related to computing resourcesmanaged by each service. Performance data may include, for example,activity log files, configuration data, operating status information,performance metrics, or any other data that is generated by the cloudcomputing services related to computing services under management.Relationship data generally may include any data that relates torelationships among the computing resources (e.g., relationship data mayindicate that a particular storage volume is attached to a particularserver instance, that a particular server instance is a member of aparticular subnet, etc.). Relationship data may be generated explicitlyby a cloud computing service or may be derived from other data generatedby the service.

In an embodiment, a cloud computing management application 1810 may beconfigured as “add-on” or “app” of a data intake and query system 108.In other examples, a cloud computing management application 1810 may bepart of another type of application, or may be implemented as astandalone application. In general, a cloud computing managementapplication 1810 enables users to configure collection of data from anynumber of cloud computing services and further includes variousinterfaces for displaying information related to a user's cloudcomputing resources.

In an embodiment, a cloud computing management application 1810 includesa data collection module 1812 and a topology map generation module 1814.A data collection module 1812 may include program logic that enablesconfiguration of one or more data sources from which data may becollected, and further enables collection of data from the configureddata sources and storage of the data for subsequent retrieval andanalysis. More detailed information about the configuration andcollection of data from cloud computing data sources is providedhereinafter in Section 3.2.

As indicated above, a cloud computing management application 1810 mayprovide various interfaces that display information about a collectionof cloud computing resources. For example, FIG. 19 illustrates anexample cloud computing key indicators interface 1900 that enables usersto view performance, security, and other metrics related to a collectionof cloud computing resources for which data collection has beenconfigured. The interface 1900, for example, comprises a dashboard whichdisplays configuration metrics 1902 (e.g., providing information about anumber of configuration changes over time), server instance metrics 1904(e.g., providing information about a total number of running, stopped,and/or reserved server instances), storage metrics 1906 (e.g., providinginformation about a total number of volumes in use, a total amount ofstorage space used, etc.), among other indicators. In general, metricsdisplayed on a cloud computing key indicators interface 1900 may begenerated based on performance data, relationship data, cost data, orany other data collected from one or more cloud computing servicesand/or other sources.

In an embodiment, a cloud computing management application 1810 furtherincludes a topology map generation module 1814 which enables thegeneration and display of interfaces which include an interactivetopology map configured to visualize a collection of cloud computingresources and relationships among the cloud computing resources. Forexample, an interactive topology map generated by a topology mapgeneration module 1814 generally may comprise a collection of nodes andedges, where each node in the map represents one or more resources ofthe collection, while each edge connecting two nodes represents arelationship between resources corresponding to the two nodes.Furthermore, in contrast to a static topology map, interactive topologymaps as described herein provide the ability for users to interactdirectly with the displayed resources (e.g., by specifying actions toperform on the resources, selecting map elements to view performancemetrics related to the corresponding resources, etc.)

FIG. 20 illustrates an interface including a topology map representing acollection of cloud computing resources and generated based on datacollected from one or more cloud computing services and/or other datasources. FIG. 20 is provided as a high-level overview of an interfacedisplaying a topology map; additional details related to the generationand interactive capabilities of such topology maps are provided inseparate sections hereinafter.

In an embodiment, a topology map interface 2000 may be included as anavailable interface of a cloud computing application 1810. The topologymap interface 2000 may be displayed, for example, in response to a userselecting menu option 2002. In an embodiment, an interface 2000 includesa topology map 2004 representing a visualization of a collection ofcloud computing resources, including a number of graphically displayednodes and edges representing individual cloud computing resources andrelationship among the resources.

In an embodiment, a topology map interface 200 includes a search panel2006, which enables users to search for particular resources and toselect particular types of resources for display in the topology map2004. For example, a search bar of the search panel 2006 may enableusers to search for nodes that match a specified label, identifier, orother search input string. Search panel 2006 may further include aselectable list of resource types that enables a user to selectparticular types of resources for display on the topology map 2004(e.g., a user may provide input selecting only virtual private clouds,server instances, and subnets for display in the topology map 2004). Asearch panel 2006 may further provide counts for each type of resourceavailable for display in the topology map 2004 (e.g., a search panel2006 may indicate that 12 different virtual private clouds, 60 serverinstances, 24 subnets, etc., are available for display in the map).

In an embodiment, a topology map interface 2002 may further includedisplay filters 2008, which enable users to filter the types ofresources displayed in the topology map 2006. For example, a user mayuse display filters 2008 to filter the displayed resources in thetopology map 2004 based on resources associated with selected useraccounts, resources associated with selected regions, resourcesassociated with selected virtual private clouds, or any other filtercriteria.

3.2. Cloud Computing Resource Data Collection

In one embodiment, a cloud computing management application 1810includes the ability to collect data related to a collection of cloudcomputing resources from one or more cloud computing services and/orother sources. The collected data generally may include any data thatprovides information about the operating status, performancecharacteristics, relationships with other resources, cost data, or anyother attributes of the resources. The data related to the cloudcomputing resources may originate from one or more cloud computingservice providers (e.g., including various types of log data generatedby the services) or may originate from other sources (e.g., fromexternal monitoring tools or other applications).

In one embodiment, one type of data related to a collection of cloudcomputing resources is referred to herein as performance data.Performance data generally refers to data that provides informationabout the status and/or performance of particular resources. Forexample, performance data may include state data which indicatesinformation about the state of one or more resources at particularpoints in time, including whether the one or more resources are active,shutdown, in a failure state, etc. For example, many cloud computingservice providers generate activity logs and other data that record eachtime resources are created, modified, or deleted, and further mayinclude information about a user associated with each action,information about when each action occurred, information about resourcefailures, etc. Performance data may further include log data indicatingperformance characteristics of one or more resources, including CPUutilization for server instances, volume IO counts for storage volumes,etc. In an embodiment, performance data may further include costinformation that may indicate, for example, financial costs related theuse of one or more resources over one or more periods of time.

In one embodiment, another type of data related to a collection of cloudcomputing resources is referred to herein as relationship data.Relationship data generally refers to data that provides informationabout relationships among one or more cloud computing resources of acollection of resources for which data has been collected. For example,relationship data may indicate that one or more server instances are amember of a particular subnet, that one or more subnets are a member ofa particular virtual private cloud, that an access control list isapplicable to one or more databases, etc. Relationship data may indicatesuch relationships among resources explicitly (e.g., the data mayindicate that a particular server instance is a member of particularsubnet) or the relationships among resources may be inferred from thedata (e.g., based on correlating resource identifiers, IP addresses,etc.).

3.2.1. Configuring Resource Data Collection

In an embodiment, a cloud computing resource management applicationprovides one or more interfaces for configuring data sources from whichperformance and/or relationship data may be collected. Each data sourcemay be a source defined by a particular cloud computing service (e.g., alog file or other data made available to users of the service) or may bea source external to any cloud computing service (e.g., based on anexternal monitoring tool). For example, a particular cloud computingservice may provide one or more APIs or other services that enable usersto retrieve various types of data generated by the service. A particularcloud computing service, for example, may generate a log file thatrecords each time a resource is created, modified, deleted, etc. In thesame log file or in a different file, the cloud computing service mayrecord information about the performance of various resources, includingCPU utilization for server instances, reads and writes for storagevolumes, etc. As indicated above, such data generally may compriseperformance data, which may indicate state and/or performanceinformation about resources, and relationship data, which may indicateinformation about relationships among various resources.

FIG. 21 illustrates an example interface screen 2100 that enables a userto configure one or more data sources for collection of data related toone or more cloud computing resources. The configuration interface 2100of FIG. 21, for example, includes an account configuration panel 2102and data source configuration panel 2104.

In an embodiment, an account configuration panel 2102 enables a user tospecify account information for one or more cloud computing services. Acloud computing service typically may restrict access to performance andrelationship data for a particular set of cloud computing resourcesbased on account credentials for an account that has permission tomanage those resources. In an embodiment, an account configuration panel2102 may enable users to configure any number of separate user accountsfor any number of separate cloud computing services. The configurationof multiple user accounts may be desirable, for example, if anorganization has computing resources associated with multiple differentaccounts (e.g., for different development teams) but desires toaccumulate the data for the separate user accounts into a singleplatform for analysis.

In an embodiment, a data source configuration panel 2106 enables a userto set configuration parameters that enables a cloud computingmanagement application 1810 to access one or more data sources. As oneexample, one data source may comprise a web service provided by a cloudcomputing service that includes an API for retrieving log files and/orother data related to a user's cloud computing resources managed by theservice. Other example data sources may include, but are not limited to,databases, data feeds, one or more files (e.g., referenced by a directlink or other reference), or any other type of accessible data. In theexample of data source configuration panel 2106, several separateconfiguration panels are provided, each corresponding to a differentdata source type (e.g., one configuration panel may correspond to a datasource providing resource configuration log files, while another panelcorresponds to a data source providing log data for network trafficflows associated with the resources, and another panel corresponds to adata source providing billing data, etc.) Separate configuration panelsmay be provided, for example, to assist with configuring the particularinputs to connect to each of the separate data sources.

In one embodiment, a cloud computing management application 1810 mayenable users to configure data sources for the collection data frommultiple different cloud computing services and/or other sources. Forexample, an organization may use two or more separate cloud computingservices, and separate data sources may be configured for each of thetwo or more separate cloud computing services. In this manner,performance data and relationship data from multiple different cloudcomputing services may be collected in a centralized platform foranalysis and for display in a single, integrated topology map display.

In an embodiment, a cloud computing management application 1810 mayfurther enable configuration of data sources to collect data related tocloud computing resources associated with multiple different regionsdefined by one or more cloud computing services. As described above, acloud computing service may enable users to create and manage resourcesthat are grouped into two or more separate geographic regions (e.g., USEast and US West) to reduce data latency and improve data redundancy,among other benefits, but users may desire to collect information formultiple different regions for display in a topology map and othervisualizations.

3.2.2. Data Collection Process

In an embodiment, based on one or more configured data sources, a datacollection module 1812 of a cloud computing management application 1810collects and stores data retrieved from the configured data sources forsubsequent analysis, generation of data visualizations, and otheroperations. For example, a data collection module 1812 may be configuredto monitor a configured data source and collect new data as the data isgenerated. As another example, one or more configured data sources maypush generated data to the data collection module 1812 on a periodicbasis, or in response to the occurrence of particular events (e.g.,creation, deletion, and/or modification of one or more computingresources).

In an embodiment, a data collection module 1812 may collect data fromone or more configured data sources and store the collected data asevents stored by a data intake and query system 108. In an embodiment,to organize the collected data into events, a data intake and querysystem may refer to one or more source type definitions to determineboundaries of events within the data, among other properties.Furthermore, a data collection module 1812 may perform variousmanipulations to collected data before storage. For example, one or moredata fields from separate data sources may be mapped to a same field forstorage by the data intake and query system 108, or new fields may becreated based on conversions or combinations of other data fields.

3.2.3. Adaptive Data Collection Process

In one embodiment, a data collection module 1812 may comprise a systemof data collection devices configured to adaptively control collectionof data from one or more external data sources. At a high level,adaptively controlling collection of data from external data sources mayinclude collecting data collection performance information related toone or more data collection nodes and, in response to analyzing thecollected performance information, adapting rates at which the datacollection nodes send data collection requests to external data sources.In this context, data collection performance information generally mayinclude, but is not limited to, network traffic data, error messagesgenerated by external data sources and/or data collection nodes,computing device performance information, and any other types ofinformation related to a data collection node's ability to collect datafrom external data sources. According to various embodiments describedherein, a data collection system may include a data collectioncontroller node configured to collect and analyze data collectionperformance information received from data collection nodes and togenerate data collection instructions for the data collection nodesbased on the analysis.

As described above in Section 3.2.1, one type of data source from whichdata collection nodes may collect data is a cloud computing serviceprovider, where a cloud computing service provider may include an APIother web service for retrieving log files and/or other informationrelated to computing resources managed by the service provider. Otherexample data sources may include, but are not limited to, databases,data feeds, one or more files (e.g., referenced by a direct link orother reference), etc. Efficient collection of data from such datasources may involve a number of challenges including throughputconstraints imposed by the data sources, network latency and/orbandwidth issues affecting data retrieval, CPU utilization and otherperformance constraints of computing devices collecting the data, andconcurrently managing different capabilities and conditions associatedwith a heterogeneous set of external data sources, etc. For example, ifdata collection requests are sent to a particular data source toofrequently (e.g., such that the rate of requests exceeds a limit imposedby the data source), data retrieval may be slowed and/or requested datamay be lost entirely. Conversely, if data collection requests are madetoo infrequently, data may not be received for ingestion and furtheranalysis in a timely manner, resulting in a degraded user experience.Thus, it generally may be desirable for a data collection module 1812 toadaptively control a rate at which data collection requests are sent toone or more external data sources so that data collection may beaccelerated as much as possible without overwhelming the external datasource, data collection nodes, and/or the interconnecting networks.

FIG. 33 illustrates a system for adaptively controlling rates at whichone or more data collection nodes send data collection requests toexternal data sources. In FIG. 33, a data collection system 3300includes a plurality of data collection nodes 3304 and a data collectioncontroller node 3306. For illustrative purposes, two separate datasources 3302, three data collection nodes 3304, and one data collectioncontroller node 3306 are depicted in FIG. 33, however, a data collectionsystem 3300 as described herein generally may include any number of datasources, data collection nodes, and data collection controller nodes.Furthermore, in other examples, a data collection controller node 3306may be integrated into one or more of the data collection nodes 3304.

In an embodiment, a data collection node 3304 may include any computingdevice that is capable of requesting and receiving machine-generateddata (e.g., system log data, network packet data, system performancedata, etc.) made available by one or more external data sources 3302. Asindicated above, the data requested from the data sources 3302 generallymay include various types of performance and/or security data related toone or more computing resources of an information technology environmentand which may be analyzed by a data intake and query system. As oneexample, if a data source 3302 represents a cloud computing serviceprovider, data requested from the data source may include performanceand/or relationship data related to computing resources managed by thecloud computing service provider. The set of external data sources 3302may include one or more of a same type of data source, and may includeone or more different types of data sources. For example, one externaldata source 3302 may represent a cloud computing service provider,another data source 3302 may represent a different cloud computingservice provider, yet another data source may represent a database logsthat are not associated with a cloud service, and so forth.

In one embodiment, data collection nodes 3304 generally may beconfigured to send data collection requests to one or more external datasources 3302, to detect data collection performance information relatedto the data collection requests, and to report the data collectionperformance information to one or more data collection controller nodes3306. For example, a data collection node 3304 may send data collectionrequests to one or more APIs or other data collection servicesassociated with one or more external data sources 3302. As indicatedabove, the data collection performance information related to the datacollection requests generally may include error messages received fromexternal data sources in response to data collection requests, networktraffic information, CPU utilization information, memory usage,information about attributes of external data sources, etc.

In one embodiment, a data collection controller node 3306 may includeany computing device configured to receive data collection performanceinformation from one or more data collection nodes 3304 and to generatedata collection instructions in response to analyzing the datacollection performance information. For example, instructions generatedby a data collection controller node 3306 may cause one or more datacollection nodes 3304 to increase, decrease, or to maintain a rate atwhich the data collection nodes sends data collection requests to one ormore data sources 3302. A data collection controller node 3306 mayinstruct a particular data collection node 3304 to decrease a rate atwhich the data collection node sends data collection requests, forexample, in response to the data collection node receiving one or moreerror messages from an external data source 3302, detecting networklatency or CPU overutilization issues, etc. Conversely, a datacollection controller node 3306 may instruct a data collection node 3304to increase a rate at which the data collection node sends datacollection requests in response to detecting an absence of any errormessages over a period of time, detecting an increase in a networklatency measurement, etc.

FIG. 34 includes a flow diagram 3400 that illustrates a process for adata collection controller to adaptively control a rate at which one ormore data collection nodes send data collection requests to one or moreexternal data sources. The various elements of flow 3400 may beperformed in a variety of systems, including systems such as describedin reference to FIG. 18. In an embodiment, each of the processesdescribed in connection with the functional blocks described below maybe implemented using one or more computer programs, other softwareelements, and/or digital logic in any of a general-purpose computer or aspecial-purpose computer, while performing data retrieval,transformation, and storage operations that involve interacting with andtransforming the physical state of memory of the computer.

At block 3402, data collection performance information is received froma data collection node of a plurality of data collection nodes. Ingeneral, the data collection performance information may be related toattempts by a data collection node 3304 to collect data from one or moredata sources 3302.

In one embodiment, a data collection node 3304 may be configured tocollect data from one or more data sources 3302 based on a datacollection policy, where the data collection policy instructs the datacollection node 3304 to request data from one or more data sources 3302based on a specified schedule. The schedule may instruct the datacollection node 3304, for example, to send data collection requests atparticular intervals (e.g., once every minute, once every hour, etc.),based on specified rate limits (e.g., send no more than ten requests anhour, send at least five requests every half hour, etc.), withinparticular time constraints (e.g., send requests only between the hoursof 8 AM and 9 PM, only on weekdays, etc.), or based on any otherscheduling constraints. In an embodiment, a data collection policy mayinclude separate instructions for each individual data source 3302, foreach different type of data source 3302, and/or for each different datacollection node 3304. As described in more detail below, a datacollection policy for each data collection node 3304 may be modified inresponse to changing conditions with respect to one or more data sources3302, data collection nodes 3304, network traffic conditions, etc.

In one embodiment, one or more aspects of a data collection policy maybe represented as a data collection profile. For example, a datacollection profile may specify various data collection instructions fordifferent types of data sources 3302 and/or types of data collectionnodes 3304. When a new data collection node 3304 joins a data collectionsystem or an existing data collection node 3304 is configured to collectdata from a new data source 3302, for example, an existing datacollection profile may be applied to the data collection node 3304. Inthis manner, a new or modified data collection node 3304 may begincollecting data from one or more data source 3302 using instructionsspecified in the data collection profile. Similar to above, a datacollection controller node 3306 may manage and update the instructionscontained in one or more data collection profiles in response tochanging conditions with respect to one or more data sources, datacollection nodes, network conditions, etc., as described in more detailhereinafter.

In one embodiment, data collection performance information may becollected (e.g., by a data collection node 3304 or other device) bymonitoring one or more aspects of a data collection process performed bythe data collection nodes. For example, each data collection node 3304may monitor network traffic speeds, network throughput, data sourceavailability, and other network statistics as the data collection nodes3304 request and retrieve data from one or more external data sources3302. As another example, data collection nodes 3304 may retrieve and/ormonitor performance information related to one or more resources of adata source 3302 (e.g., devices of a data source 3302 from which therequested data is retrieved). As yet another example, each datacollection node 3304 may monitor and record its own performanceinformation during data collection such as, for example, CPU utilizationinformation, memory performance, availability information, etc. Datacollection nodes 3304 may send collection performance information to adata collection controller node 3306 as the collection performance datais generated, in response to the occurrence of particular events, and/orthe nodes 3304 may collect and send the data to a controller node 3306on a periodic basis.

In one embodiment, data collection nodes 3304 may perform one or moretransformations to some or all of the data collection performanceinformation prior to sending the information to a data collectioncontroller node 3306. For example, each data collection node 3304 may beconfigured to normalize error messages, exception messages, etc.,received from particular data sources 3302 into a defined and common setof error codes. For example, similar types of errors (e.g., a throttlingexception, denial of service exception, request timed out exception,etc.) which may be received from various different data sources 3302 canbe translated into a common error code set in order to facilitateanalysis by a collection node 3306. In other examples, a data collectionnode 3304 may send data collection performance information substantiallyunaltered to a controller node 3306 and the controller node maydetermine type of errors and other data present in the performanceinformation.

At block 3404, based on the data collection performance informationreceived from the data collection node, instructions may be generatedfor one or more data collection nodes to modify a rate at which the datacollection nodes send data collection requests to one or more externaldata sources. For example, based on a data collection controller node3306 analyzing the data collection performance information received fromone or more data collection nodes 3304, the data collection controllernode may generate instructions to cause one or more data collectionnodes to increase, decrease, or maintain a rate at which the datacollection node(s) send requests to one or more data sources 3302.

In one embodiment, based on an analysis of data collection performanceinformation received from one or more data collection nodes 3304, a datacollection controller node 3306 may determine that one or more datacollection nodes 3304 can increase a rate at which the data collectionnode(s) send data collection requests to one or more data sources 3302.For example, a controller node 3306 may determine from the datacollection performance information that previously detected networktraffic issues, computing device performance issues, or other issueshave been resolved or mitigated and thus the collection nodes 3304 canincrease a rate of data collection. Similarly, a data collectioncontroller node 3306 may identify in data collection performanceinformation one or more error messages, network traffic issues,computing device performance issues, etc., for which it may bebeneficial if one or more data collection nodes 3304 decrease a rate atwhich the nodes send data collection requests to one or more datasources 3302. In these examples, a data collection controller node 3306thus may generate instructions for one or more data collection nodes3304 to increase or decrease a data collection rate relative to one ormore data sources 3302.

In one embodiment, a data collection controller node 3306 may determinethat one or more data collection nodes 3304 can increase a rate at whichthe nodes send collection requests in response to determining that adata source 3302 has increased a rate at which the data source permitsor is otherwise capable of receiving data collection requests. Forexample, a particular data source 3302 initially may limit a rate atwhich data may be requested from the data source to prevent the datasource from being flooded with requests from collection nodes or forother reasons. In this example, if a data collection node 3304 attemptssend data requests at a rate that exceeds the request limit imposed bythe data source, the data source may return error messages or drop therequests entirely. However, a data source subsequently may increase arate at which the data source permits data requests, for example,because the data source has increased its computing capacity to receivesuch requests.

In one embodiment, a data collection controller node 3306 may generatedata collection instructions in response to determining that an externaldata source 3302 has increased a rate at which it permits datacollection requests by analyzing various operational aspects of the datasource 3302 and/or related components. In one example, a data collectioncontroller node 3306 may detect that an external data source hasincreased a permitted data request rate in response to a collection node3304 and/or controller node 3306 receiving a status message from thedata source indicating that the request limit has increased. As anotherexample, a controller node 3306 may detect an updated status or versionidentifier included in data sent back to a collection node in responseto a data collection request (e.g., response messages previouslyindicating a version of “API v1.1” has changed to “API v.1.2”).

As yet another example, a data collection controller node 3306 maydetermine that a data source 3302 has increased a permitted data requestrate by detecting an operational pattern that may be associated with anupgrade to an external data source. For example, upgrades to a datasource 3302 typically may occur at particular times (e.g., 2:00 AM-4:00AM) and if a data collection controller node 3306 detects that resourcesof a data source 3302 briefly go offline or that throughput suddenly isreduced during such a time, the controller node 3306 may determine thata data source has potentially increased a permitted data collectionrate. As another example, a data collection node 3306 may detect anupgrade based on information published to an external data source suchas a website, web feed, or any other data source that may be separatefrom the external data source. Based on any of the examples describedabove and others, a data collection controller node 3306 may generateinstructions that cause one or more data collection nodes 3304 toincrease a data collection request rate in response.

In one embodiment, a data collection controller node 3306 mayperiodically analyze data collection performance information receivedfrom one or more data collection nodes 3304 and update the instructionsfor each data collection node iteratively over time. For example, a datacollection controller node 3306 may analyze incoming performanceinformation and send updated instructions to collection nodes 3304periodically (e.g., once every minute, five minutes, hour, etc.) inresponse to detected changes. In other examples, a data collectioncontroller node 3306 may send updated instructions in response todetecting particular conditions in the data collection performanceinformation (e.g., in response to detecting a certain number of errormessages, detecting that a network or computing device performancemeasurement has changed by a threshold amount, etc.). In an embodiment,a data collection controller node 3306 may use any combination ofmachine learning techniques and other algorithms to analyze receivedcollection performance information to determine an optimal datacollection request rate for one or more data collection nodes 3304.

At block 3406, the instructions are sent to the data collection node.For example, a data collection controller node 3306 may generate andsend the instructions to one or more data collection nodes 3304 causingthe data collection nodes to increase, decrease, or maintain a rate atwhich the collection nodes send data collection requests to one or moredata sources 3302. In one embodiment, a data collection controller node3306 may send updated data collection instructions to a single datacollection node or to a plurality of data collection nodes. For example,if a data collection controller node determines that a change hasoccurred with respect to a particular external data source from whichtwo or more data collection nodes request data, instructions may be sentto each of the devices to update the collection policy globally. Inother examples, data collection nodes 3304 may be configured toperiodically retrieve a current set of data collection instructions froma data collection controller node 3306.

3.3. Cloud Computing Resource Topology Maps

As indicated above, in one embodiment, a cloud computing managementapplication 1810 may be configured to generate topology mapvisualizations of cloud computing resources based on data collected andstored by a data collection module 1812. At a high level, a topology mapvisualization includes a graphical display of a set of nodes, eachrepresenting one or more cloud computing resources, and edges, eachrepresenting a relationship between two or more cloud computingresources.

3.3.1. Generating Topology Map Displays

In one embodiment, a topology map generation module 1814 may beconfigured to generate data providing instructions for displaying acollection of cloud computing resources as a topology map and to causedisplay of the topology map based on the generated data. As illustratedin reference to FIG. 20, for example, a topology map generation module1814 may generate and cause display of a topology map in response to auser requesting display of a topology map interface provided by a cloudcomputing management application 1810. In an embodiment, generating dataproviding instructions for displaying a topology map generally mayinclude retrieving collected performance and relationship data andprocessing the data for use by a topology map display engine.

In one embodiment, processing collected performance and relationshipdata generally may involve converting the collected data to a formatthat suitable for use by one or more data visualization libraries. Forexample, a topology map generation module 1814 may include one or moredata visualization libraries that are configured to receive input datadescribing a collection of cloud computing resources and relationshipsamong the resources and to generate a graphical display of a topologymap based on the input data. Thus, a topology map generation module 1814may convert collected performance and relationship data into a table orother data format, for example, that specifies relationships among thecollection of cloud computing resources, along with any otherinformation that may be used in the display. In one embodiment, if cloudcomputing management application 1810 includes a web-based interface, adata visualization library may provide resources for displaying theprocessed set of data as a topology map using HTML, SVG, and/or otherstandards for displaying visualizations in a web browser.

In an embodiment, a topology map generation module 1814 may retrieveperformance data and relationship data to generate a topology mapon-demand (e.g., in response to receiving a user request to display atopology map interface) and/or the module may be configured toperiodically retrieve and pre-process the data for display. For example,topology map generation module 1814 may be configured to run a searchperiodically (e.g., every 2 hours) to retrieve the most recentlycollected performance and relationship data, and to use the retrieveddata to precompute the display information for the topology map. Byprecomputing the display information, a topology map interface may begenerated more quickly when requested.

As illustrated in FIG. 20, for example, the topology map displayincludes a set of interconnected nodes and edges representing acollection of cloud computing resources. Each of the individual nodesand edges of a topology map generally may be placed at any location onthe canvas; however, random placement of the nodes may result in acomplex visualization that makes it difficult to understand theunderlying relationships among the nodes. In one embodiment, a topologymap generation module 1814 may be configured to display nodes on the mapin a more intuitive manner, for example, so that similar resources aredisplayed near one another and generally arranged in a manner thatprovides for a more aesthetically pleasing display. For example, atopology map generation module 1814 may be configured to place nodesrepresenting a set of server instances that are a member of the samesubnet in close proximity to one another and without overlapping edgesso that the group of instances may be readily located. As anotherexample, each cluster of connected resources (e.g., each separatevirtual private cloud and associated resources) may be displayed in anon-overlapping fashion to facilitate identification of each separate ofcluster.

In one embodiment, a topology map generation module 1814 may be furtherconfigured to display various elements of a topology map, including someor all of the nodes and edges, using particular graphical elements thatcorrespond to various attributes related to the represented computingresources. As one example, each node in the topology map may bedisplayed using a particular graphical element depending on a type ofresource represented by the node. For example, a node that represents avirtual private cloud may be displayed using a cloud icon, while anothernode on the same display representing a storage volume may be displayedusing a disk symbol, etc. As another example, each different type ofresource may be displayed using a different color or icon size torepresent each different type of resource.

In an embodiment, various elements of a displayed topology map may bedisplayed using particular graphical elements based on data related tothe performance, operating state, cost utilization, or other metricsrelated to each resource. As one example, a topology map generationmodule 1814 may be configured to display a topology map where nodesrepresenting server instances that are currently above a particular CPUutilization level are displayed using one type of graphical element(e.g., a flashing red icon), whereas other nodes representing serverinstances that are currently below the particular CPU utilization levelare displayed using a different graphical element (e.g., a static grayicon). As another example, nodes representing server instances that arecurrently active and running may be displayed using one color, whileserver instances that are currently shutdown and/or currently not in usemay be displayed using another color. In an embodiment, a default set oftopology map element graphics and display criteria may be provided by acloud computing management application 1810 and/or may be customized bya user as desired.

3.3.2. Interacting with Topology Map Displays

In one embodiment, in addition to the display of topology map elementsrepresenting a collection of cloud computing resources, a graphical userinterface displaying a topology map may be configured to enable userinteraction with the resources represented in the topology map. Manydifferent types of interactions may be possible, including visuallynavigating the topology map (e.g., zooming, panning, and otherwisealerting the display of the topology map), selecting map elements todisplay status, performance, and/or relationship information related tothe corresponding resources, selecting map elements to specify actionsto perform relative to the corresponding resources, etc. In oneembodiment, interactions with elements of the topology map may be linkedto the corresponding cloud computing services such that actionsspecified using a displayed topology map may result in performance ofthose actions at the respective cloud computing service(s). In general,an interactive topology map as described herein provides the ability tonot only view visualizations cloud computing resources, but to alsoanalyze and manage operation of the resources in a highly efficientmanner.

In one embodiment, an interface displaying a topology map is configuredto receive input selecting one or more map elements and to displayinformation about the resources represented by the selected elements. Asone example, the interface may be configured such that if input isreceived indicating that the user is hovering a mouse pointer over aparticular map element, information about the corresponding cloudcomputing resource may be displayed near the selected element. Examplesof information that may be displayed include, but are not limited to, aresource identifier (e.g., a unique identifier generated by a cloudcomputing service for the resource), a resource type (e.g., whether theresource is a server instance, virtual private cloud, storage volume,etc.), a name of the resource (e.g., a user assigned label for theresource), a region associated with the resource, a status of the device(e.g., whether the resource is running, shutdown, in a failure state,etc.), key performance indicator (KPI) values associated with theresource, etc. If a device is in a particular operating state (e.g., ifa server instance is currently shutdown), additional information may bedisplayed such as how long the instance has been shut down, the userthat caused the shutdown, cost information associated with the instance,etc.

In one embodiment, similar to displaying information in response to theselection of a topology map node, a topology map interface may beconfigured to receive input selecting one or more edges in the map andto display information about the selected edge(s). For example, if aparticular edge connects a first node representing a first serverinstance to another node representing a subnet, information aboutnetwork traffic transferred to and from the server instance may bedisplayed. Other examples of information that may be displayed about aparticular edge include, but are not limited to, information about theorigin and/or destination of network traffic, network traffic statistics(ratio of accept, deny, etc.), or any other information related to therelationship between the connected nodes.

FIG. 22 illustrates an interface including a topology map, where inputis received indicating a desire to display information about aparticular map element. In the interface 2200 of FIG. 22, for example, aparticular node 2202 has been selected (e.g., by hovering over the node,clicking on the node, etc.) and, in response, an information panel 2204is displayed. The information panel 2204 may include various informationabout the server instance represented by the selected node including,but not limited to, an identifier of the server instance, a type of theresource, a name or label for the server instance, an account IDassociated with the server instance, a region associated with the serverinstance, a status of the server instance, and a type of serverinstance. A user may subsequently select another node in the graph tochange the display of the information panel 2204 to information aboutthe newly selected node.

In an embodiment, an interface displaying a topology map may be furtherconfigured to receive input selecting a particular map element fordisplay of more detailed information related to the selected element.For example, in response to a selection of a particular node, a moredetailed information panel may be displayed, or a separate side panelmay be displayed. The more detailed information may include, forexample, information indicating relationships between the correspondingresource and other resources, performance metrics related to theselected resource, and activity log information (e.g., informationindicating when a server instance was created, last restarted, etc.).The performance metrics may correspond to a particular time period(e.g., for the past week or past month) or display information for theentire lifespan of the resource.

FIG. 23 illustrates an example interface 2300 displaying a panel 2304providing detailed information about a selected map element 2302.Relative to the information panel 2204 depicted in FIG. 22, for example,a side panel display 2304 includes a more detailed set of informationfor a selected resource, including information about relationships toother resources, a line chart indicating a CPU utilization percentageover time, and an activity count for a particular time period. Ingeneral, a more detailed information panel may display any performancemetrics and other information derived from performance data and/orrelationship data collected for the particular resource. In oneembodiment, one or more items of information displayed in a side paneldisplay 2304 may be selected by a user to cause display of other relatedinterfaces, e.g., other dashboards displaying related metrics,interfaces displaying events and associated raw data, and/or otherinterfaces external to the cloud computing application.

In one embodiment, a topology map interface may be configured to displaycost information associated with one or more selected topology mapelements. For example, a user may select a node representing a serverinstance and, in response, a topology map interface may display costinformation for the server instance such as, for example, a total costincurred by the server instance, an estimated current bill amount, anaverage cost for the server instance per month, etc.

In an embodiment, a topology map interface may also be configured todisplay cost efficiency information for selected map elements. Forexample, many cloud computing services offer various types of the samecomputing resource based on different payment models. For example, acloud service provider may offer three or more different types of serverinstances such as “on-demand” instances, “reserved” instances, and“spot” instances, the cost benefits of which depend on how the serverinstances are used. In an embodiment, based on a determined type ofserver instance and performance information associated with theinstance, a topology map interface may display information indicatingwhether the type of server instance being used is the most costeffective of the available types of server instances. Although theexamples above illustrate display of cost information for serverinstances, similar information may be displayed for selected storagevolumes, network interfaces, or any other cloud computing resources.

In one embodiment, in response to a selection of multiple topology mapelements, an interface may be configured to display aggregateinformation related to the resources represented by the selected mapelements. As one example, the displayed aggregate information for a setof selected nodes may indicate information common to the resources(e.g., that the corresponding resources were created by the same useraccount, that the resources were created at or around the same date,etc.). As another example, aggregate information may include informationspecific to each of the selected resources displayed together (e.g., alist of other resources related to any of the selected resources, a listof IP addresses associated with the resources, etc.). As yet anotherexample, aggregate information may include one or more metrics derivedfrom information associated with the selected resources (e.g., anaverage response time for a set of selected server instances, a totalcost incurred by a set of selected resources, a total number ofconfiguration changes made with respect to the selected resources,etc.).

In one embodiment, in response to a selection of one or more elements ofa displayed topology map, a cloud computing management application 1810may be configured to cause display, on the same interface or in aseparate interface, of one or more events related to the computingresources represented by the one or more nodes. For example, a user mayselect a node representing a particular server instance and furtherselect a menu option or provide other input indicating a desire to viewstored events associated with the server instance. In response toreceiving the input, application 1810 may be configured to display anevents list (e.g., similar to events list 608 of FIG. 6) or otherinterface that enables the user to view the raw data and otherinformation of associated events. Similarly, an application 1810 may beconfigured to enable users to select indications of particular resources(e.g., a name of a particular server instance, storage volume, etc.)from an events list or other display (e.g., by clicking on a label orother indicator of a particular resource included in the displayed eventdata) and, in response cause display of a topology map view thatincludes a node representing the selected resource.

In an embodiment, a cloud computing management application 1810 may beconfigured to enable users to specify actions to perform relative to oneor more resources represented in a displayed topology map. In oneembodiment, some or all of the actions may result in the application1810 causing a cloud computing service to perform the specified actionsrelative to one or more selected resources. As one particular example, auser may select a node representing a server instance and provide inputrequesting to shut down the server instance. In response to the input, acloud computing management application 1810 may send a request (e.g.,issue an API call, executable command, upload a script file, a triggercallback, etc.) to the corresponding cloud computing service to causethe selected server instance to be shutdown. The requested action maythen be reflected both in the topology map (e.g., by changing the colorof the corresponding node or removing the node from the map) and at thecloud computing service where the server instance is actually shut down.

In one embodiment, an interface displaying a topology map may beconfigured to enable users to provide input specifying notes and/or tagsto associate with one or more elements of the topology map. A note, forexample, may include text, images, links, or other user-generatedcontent that a user desires to associated with map elements. A tag mayrepresent a keyword or term assigned to one or more map elements, whichmay be used to group certain elements and enable groups of items to bemore easily searched. In one embodiment, if a cloud computing serviceassociated with the respective topology map elements supports theaddition of notes and/or tags to resources within its platform, theaddition of a note and/or tag on the topology map further may cause theapplication 1810 to generate a request to the cloud computing service toadd the specified note and/or tag in association with the selectedresources. In one embodiment, to indicate a desire to associate notesand/or tags with one or more resources, a user may select one or morenodes and/or edges of the topology map representing the resources ofinterest.

FIG. 24 illustrates a portion of an example interface which enablesusers to associate notes and/or tags with one or more elements of atopology map. FIG. 24 includes several topology map elements, includingselected nodes and edges 2402. Each of selected nodes and edges 2402 inFIG. 24, for example, may represent a separate server instance andassociated relationships. As depicted in FIG. 24, input may be providedto the interface selecting a subset of the map elements (e.g., byclicking on one or more nodes, drawing a design around the desirednodes, etc.) and a menu option may be selected or other input providedindicating a desire to add a note and/or tag to the selected nodes. Anote panel 2404, for example, illustrates an example interface elementthat may be displayed enabling users to enter freeform text, images, taglabels, links, or any other information that a user desires to associatewith a set of nodes. In an embodiment, if any associated cloud computingservices supports the association of notes and/or tags with the selectedcomputing resources, a request may be send the cloud computing servicesto cause the service to store any note and/or tag information providedto a note panel 2404.

In an embodiment, an interface displaying a topology map may beconfigured to enable users specify one or more triggers to associatewith selected map elements. In this context, a trigger generally refersto one or more programmed actions that may be executed in response to anoccurrence of one or more specified conditions. In the context of aserver instance, example triggers that may be configured include causingthe server instance to startup at a particular time each day, to shutdown in response to detecting that the CPU utilization drops below acertain level, etc. A trigger may be specified for a particular resource(e.g., for a single server instance, storage volume, etc.) or for agroup of resources.

FIG. 25 illustrates a portion of an example interface 2500 forspecifying one or more triggers to associate with one or more selectedelements of a topology map. For example, in response to an interface2500 receiving input indicating a desire to create a trigger for one ormore selected map elements 2502, a trigger configuration panel 2504 maybe displayed enabling a user to specify a trigger condition, a time foractivation of the trigger, a trigger operation, etc. In one embodiment,a trigger operation may be specified using one or more commands inputusing the panel 2504, or as part of a script or other file thatspecifies the trigger actions to execute in response to detecting thetrigger conditions.

In one embodiment, an interface displaying a topology map may beconfigured to receive input specifying one or more control operations toperform relative to one or more selected resources. Examples of controloperations that may be specified for one or more resources include, butare not limited to, scheduled jobs, instant commands, etc. For example,a user may directly control one or more resources displayed a topologymap display by indicating actions such as shutdown, restart, disconnect,etc.

In one embodiment, an interface displaying a topology map may beconfigured to receive input specifying one or more actions to apply tomultiple selected topology map elements. For example, a user may selecta set of nodes corresponding to a set of server instances and select anoption to shut down all of the selected instances. In one embodiment, inresponse to receiving a selection of one or more topology map elements,an interface displaying a topology map may be configured to present oneor more selectable options which are relevant to the resourcescorresponding to the select map elements. For example, if a user selectsa plurality of nodes corresponding to server instances, a full set ofoptions may be presented related to actions that can be taken withrespect to the server instances (e.g., shutdown, restart, etc.).However, if a user selects a heterogeneous set of nodes (e.g., one noderepresenting a server instance and another node representing a storagevolume), a set of options relevant to all of the nodes may be presented(e.g., a backup option may be applicable to both server instances andstorage volumes).

In one embodiment, input received specifying any of notes, tags,triggers, actions, etc., to associate with one or more resources mayinvolve resources that are associated with two or more different cloudcomputing services. For example, input may be received selecting a firstnode representing a first server instance managed by a first cloudcomputing service and a second node representing a second serverinstances managed by a second cloud computing service. The input mayfurther include specification of a note, tag, trigger, etc., to apply toboth of the first server instance and second server instance. Inresponse to receiving the input, the cloud computing managementapplication 1810 may send requests to both the first cloud computingservice and the second cloud computing service to perform the specifiedaction(s) at the respective services.

In one embodiment, an interface displaying a topology map may beconfigured to receive input selecting one or more nodes and moving theone or more nodes from one location on the topology map to anotherlocation on the topology map and, in response, causing one or morerelationships between the nodes to change. For example, a user mayselect a particular node representing a server instance associated afirst subnet and drag and drop the particular node at a location near asecond subnet. In response, a request may be sent to an associated cloudcomputing service to move the server instance from the first subnet tothe second subnet.

In an embodiment, a topology map interface may enable users to selectone or more edges displayed in a topology map and to specify an actionto be applied to all of the nodes connected by the selected edges. Forexample, a user may select an edge connecting a first node representinga server instance and a second node representing a storage volumeattached to the server instance, and further select an option to backupthe connected resources. In response, the cloud computing applicationmay send a command to a cloud computing service to backup both theserver instance and the storage volume. As another example, an interfacemay enable users to select a particular node and apply an action to anyother node connected to the particular node by an edge. For example, auser may select a particular node of a topology map representing asubnet, where the particular node is connected to a plurality of serverinstances by a plurality of edges. The user may further specify anaction (e.g., startup, shutdown, backup, etc.) that may then be appliedto all of the resources connected to the selected node.

In one embodiment, a cloud computing application may be configured todisplay information related to portions of a topology map that mayrepresent underutilized resources and/or resources used in aninefficient manner from a cost perspective. For example, a cloudcomputing application 1810 may include a set of cloud computing “bestpractices” or guidelines that indicate information related to efficientuse of particular types of cloud computing resources. In response todetecting that one or more specified guideline warning conditions aremet (e.g., in response to detecting that a server instance of aparticular type is being over utilized), one or more alerts or otherdisplays may be presented to the user.

3.3.3. Analyzing Displayed Topology Maps

In one embodiment, a cloud computing management application 1810 enablesusers to export data related to a displayed topology map. For example,in response to receiving a selection of one or more topology mapelements, data related to the selected map elements may be retrieved andconverted to one or more export formats. If the selected map elementscorrespond to one or more server instances, for example, data selectedfor export may include performance metrics, cost and/or billinginformation, or any other stored information related to the selectedserver instances. In one embodiment, further input may be receivedspecifying particular fields for extraction, combinations of fields, andother data export preferences.

FIG. 26 illustrates a portion of an example interface 2600 for selectingone or more topology map elements and providing input to export datarelated to the selected elements. The interface 2600 includes a set ofselected nodes and edges 2604 corresponding to a set of server instancesand further includes an export panel 2604. In an embodiment, exportpanel 2604 provides one or more options for exporting data related tothe computing resources represented by the selected map elements. A usermay use an export panel 2604, for example, to specify particular datafields to export (e.g., resource identifiers, creation dates, costand/or billing information, etc.), a value pattern to filter ortransform the exported data, and a file format for the exported data(e.g., a CSV file, tab delimited file, database file, etc.). A valuepattern may be specified, for example, as a regular expression or othertype of pattern matching expression used to filter one or more exporteddata fields. As one example, if a user has selected several serverinstance nodes and specified a field “instanceType” for export, the usermay further specify a value pattern of “instanceType=c4.*large” tofilter the exported results to those server instances having an“instanceType” matching the regular expression “c4.*large”. In thisexample, data for selected server instances with instanceTypes of“c4.xlarge”, “c4.4xlarge”, and “c4.8xlarge” may be exported, while datafor other server instances with example instanceTypes of “m4.xlarge” or“c2.xlarge” may not be exported.

In one embodiment, an interface displaying a topology map may enableusers to generate other types of data visualizations based on selectedmap elements. For example, FIG. 27 illustrates a portion of an exampleinterface 2700 that enables users to select one or more topology mapelements and provide further input to generate another types of datavisualizations based on the selected elements. For example, FIG. 27includes data transformation panel 2702 and a visualization settingspanel 2704 which enable a user to provide input specifying particularfields to export, a value pattern, a visualization type (e.g., a barchart, a line chart, a pie chart, etc.) for selected map elements 2706.Depending on a type of visualization selected in transformation panel2702, for example, a visualization settings panel 2704 may enable usersto provide additional information including data to include on each ofan X-axis and a Y-axis for a bar chart, chart boundaries, data ordering,etc. In this manner, users may easily select topology map elements ofinterest and generate different types of visualizations for selectedelements to gain different perspectives on the data.

3.3.4. Displaying Time-Based Topology Maps

In one embodiment, a cloud computing management application 1810 enablesdisplay of animated topology maps which provide visualizations of how acollection of cloud computing resources and relationships among theresources change over time. Examples of such time-based topology mapdisplays may include, but are not limited to, display of topology mapsat specified points in time, animated topology maps displaying anevolution of a collection of resources over a period of time, andcomparison topology maps displaying differences between a topology mapat two or more particular points in time.

In one embodiment, a cloud computing management application 1810provides one or more interfaces configured to receive input specifying aparticular point in time and to cause display of a topology maprepresenting the state of a collection of cloud computing resources atthe specified point in time. For example, referring again to FIG. 20, atopology map interface 2000 may include time-based display controls 2010that enable a user to specify a particular point in time, for example,by using a time slider or a calendar input component. In response toreceiving input specifying a particular point in time, a topology mapgeneration module 1814 may be configured to retrieve collectedperformance and/or relationship data for the specified point in time andto generate a display of the topology map corresponding to the specifiedpoint in time.

In one embodiment, a topology map generation module 1814 enables displayof animated topology maps, also referred to herein as “time-lapse”displays, which display a series of topology maps over one or moreperiod of times. A time-lapse display of a topology diagram may, forexample, result in a movie-like display that enables users to betterunderstand how a collection of cloud computing resources andrelationships among the resources evolve over time.

FIG. 28 illustrates a portion of an example interface for displaying atime-lapse of a topology map. The interface 2800 of FIG. 28 includes atopology map 2802, a time slider 2804, and time-lapse play controls2806. The topology map 2802, for example, includes a number of nodes andedges representing various cloud computing resources and relationshipsamong the resources. More specifically, the topology map 2602 displayedin FIG. 28 represents a state of the cloud computing resources at aparticular time t.

In one embodiment, a time slider 2804 may be configured to allow usersto navigate, or “scan,” the topology map across points in time. Forexample, the current state of time slider 2804 may be set the displaythe topology map 2802 at a time a. A user may drag the slider 2804 tothe right, causing the displayed topology map 2802 to advance in time toa time b, where the display of the topology map at time b includesdisplay of the state of the resources at that time. For example, if oneor more computing resources were added, deleted, or modified, one ormore corresponding nodes of the displayed topology map 2802 may beadded, deleted, and/or modified to reflect the changes.

In an embodiment, play controls 2806 may enable a user to cause atime-lapse or movie-like display of the topology map 2802 over a periodof time. For example, instead of manually advancing the display of thetopology map 2802 through time, a user may select the “play” button ofplay controls 2806 and the display of topology map 2802 mayautomatically animate in a manner that depicts changes with respect tothe displayed resources over a chronological period of time. A user mayuse other interface elements of play controls 2806 to pause the playbackof a topology map time-lapse, rewind the playback, restart the playbacketc. As described above in reference to Section 3.2, a topology mapgeneration module 1814 may be configured to precompute displayinformation corresponding to some or all of the points of time of thetime-lapse display to facilitate smoother playback of the time-lapse.

In one embodiment, during playback of a topology map time-lapse, a usermay provide input to mark two different points in time of the playback(e.g., if a time-lapse corresponds to the changes of a topology map overa month-long time period, a user may select two particular points intime during the month). Based on the marked points in time, the user mayfurther provide input to generate a comparison topology map displaysthat displays differences between the topology map at the marked pointsin time (e.g., indicating nodes and/or edges that are added, removed,and/or modified).

In an embodiment, during playback of the topology map time-lapse, aninterface displaying a topology map time-lapse may display an indicationof a time associated with each portion of the playback. For example, asthe playback of a time-lapse progresses, an indication of a dateassociated with each of the displayed “frames” of the time-lapse may bedisplayed in association with the topology map so that a user can bettertrack when the associated events in the time-lapse actually occurred.

In one embodiment, a cloud computing management application may enabledisplay of an animated topology map (e.g., a time-lapse display)synchronized with other data visualizations. For example, a user maydesire to view an animated, time-lapse display of a topology map insynchronization with one or more other visualizations that provideperformance metrics, cost and/or billing information, or otherinformation related to the depicted resources across the displayedpoints in time. Examples of other data visualizations that may bedisplayed in conjunction with an animated topology map include linecharts (e.g., displaying CPU utilization levels, network traffic levels,and/or cost information over time). In this example, a topology mappingmodule may enable display of a response time line chart to the topologydiagram that enables a user to more easily determine how the number ofinstances affects response time.

FIG. 29A illustrates a portion of an example interface 2900A displayinga topology map 2902A in synchronization with a separate datavisualization 2904A. Similar to FIG. 28, a user may use a time sliderand/or play controls 2906A to play a time-lapse of the topology map2902A and/or manually advance the topology map 2902A to particularpoints in time. In an embodiment, in synchronization with the display ofthe topology map 2902A at the particular points in time, performancemetrics and other information may be displayed in the separatevisualization 2904A. In the example of FIG. 29A, the separatevisualization 2904A displays a line chart indicating an aggregateresponse time of the resources depicted in the topology map 2902A. Ingeneral, a synchronized data visualization may be any type of datavisualization, including line charts, bar charts, pie charts, etc.

FIG. 29B illustrates display of the same example interface 2900Adisplaying a topology map 2902B, representing the same collection ofresources displayed in topology map 2902A of FIG. 29A but at a differentpoint in time. For example, the display of topology map 2902B may occura few seconds after a user starts a time-lapse display for the topologymap, or in response to a user advancing the topology map display to aparticular point in time using the time slider.

In FIG. 29B, for example, two additional nodes 2906 are displayed in thetopology map 2902B relative to the topology map 2902A, representing twonew server instances that were created in the intervening time period.For example, the new server instances may be a part of an “auto scalinggroup” of server instances are intended to increase or decrease innumber depending on demand. As illustrated by the updated separatevisualization 2904B, the addition of the new server instancescorresponded with a decrease in the aggregate response time of thecollection of resources. The synchronization of additionalvisualizations with a time-lapse display of a topology map may provideeven greater insight into the cause and effects of certain changeswithin the topology of a collection of cloud computing resources.

In one embodiment, an interface may be configured to display acomparison topology map that graphically depicts a comparison between atopology map at a first point in time and the same topology map at asecond point in time. For example, a topology map interface may beconfigured to receive input specifying two separate points in time forcomparison (e.g., corresponding to two separate points of time ofinterest to a user). In response to receiving the input specifying theseparate points in time, the interface may be configured to display atopology map corresponding to the state of a collection of computingresources at the earlier specified point in time, and to displayadditional information on the topology map representing differences inthe topology map at the later specified point in time. For example, oneor more nodes and edges may be displayed using particular colors orgraphics to indicate that the corresponding computing resources werecreated, deleted, and/or modified during the time period between theearlier point and time and the later point in time.

FIG. 30 illustrates a portion of an example interface displaying atopology map indicating differences between states of the topology mapat two different points in time. The interface 3000 of FIG. 30, forexample, includes a number of nodes and edges representing a collectionof cloud computing resources and relationships among the resources,where each of the nodes in the map is displayed using a particulargraphical element depending on a state of the node relative to anotherpoint in time. For example, nodes representing resources that have beenadded to the topology map may be displayed using a first color, nodesrepresenting resources that have been deleted from the topology map maybe displayed using a second color, and nodes representing resources thathave been modified may be displayed using a third color.

4.0. IMPLEMENTATION EXAMPLES

FIG. 31 is a flow diagram 3100 that illustrates generation of agraphical user interface displaying a topology map, according to anembodiment. At block 3102, a cloud computing management applicationreceives performance data related to performance of a plurality ofcomputing resources managed by one or more cloud computing services, andrelationship data related to relationships among the plurality ofcomputing resources managed by the one or more cloud computing services.For example, a data collection module 1812 of a cloud computingmanagement application 1810 may receive the performance data and therelationship data based on one or more configured data sources.

At block 3104, the cloud computing management application causes displayof a graphical user interface including a topology map generated basedon the performance data and the relationship data, the topology mapincluding a plurality of nodes representing the plurality of computingresources, and one or more edges representing the relationships amongthe plurality of computing resources. For example, referring to FIG. 20,an interface 2000 may display a topology map 2004 including any numberof nodes, each representing one or more computing resources, and edges,each representing a relationship among the computing resources.

FIG. 32 is a flow diagram 3200 that illustrates generation of agraphical user interface displaying an animated topology map, accordingto an embodiment. At block 3202, a cloud computing managementapplication retrieves state data related to a state of a plurality ofcloud computing resources at each of a plurality of points in time, thestate data including information related to one or more of:relationships among the plurality of cloud computing resources, andperformance information related to one or more cloud computing resourcesof the plurality of cloud computing resources. For example, the statedata may include data stored by a data collection module 1812 based onperformance and/or relationship data received from one or moreconfigured data sources.

At block 3204, the cloud computing management application generates, foreach of the plurality of points in time, topology map data based on thestate data for the point in time, the topology map data providinginstructions for displaying the one or more cloud computing resources asa topology map. For example, a topology map generation module 1814 maygenerate the topology map data for use by one or more data visualizationlibraries configured to display topology maps based on the topology mapdata.

At block 3206, the cloud computing management application causes displayof an animated topology map on a graphical user interface, the animatedtopology map displaying a series of topology maps generated based on thetopology map data for each of the plurality of points in time. Referringto FIG. 28, for example, the animated topology map may be displayed onan interface that enables users to play a time-lapse of a topology map.

5.0. EXAMPLE EMBODIMENTS

Examples of some embodiments are represented, without limitation, in thefollowing clauses:

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving performance data related to performance of aplurality of computing resources managed by one or more cloud computingservices, and relationship data related to relationships among theplurality of computing resources managed by the one or more cloudcomputing services; causing display of a graphical user interfaceincluding a topology map generated based on the performance data and therelationship data, the topology map including a plurality of nodesrepresenting the plurality of computing resources, and one or more edgesrepresenting the relationships among the plurality of computingresources.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least a portion of the performance data isreceived from the one or more cloud computing services.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least a portion of the relationship data isreceived from the one or more cloud computing services.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the topology map is an interactive topology map.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one computing resource of the plurality ofcomputing resources is one of: a virtual private cloud, a serverinstance, a subnet, a storage volume, a network interface, an accesscontrol list (ACL), a route table, and an internet gateway.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one relationship between a first computingresource and a second computing resource is one of: the first computingresource is a member of a subnet represented by the second computingresource, the first computing resource is a member of a virtual privatecloud represented by the second computing resource, the first computingresource is a storage volume associated with the second computingresource, and the first computing resource is a member of a securitygroup represented by the second computing resource.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one node of the plurality of nodes isdisplayed using a particular graphical element based on performanceinformation associated with the computing resource represented by the atleast one node.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one edge of the one or more edges isdisplayed using a particular graphical element based on performanceinformation associated with a network connection between the computingresources represented by the nodes connected by the at least one edge.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one edge of the one or more edges isdisplayed using a particular graphical element based on relationshipinformation associated with the computing resources represented by thenodes connected by the at least one edge.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input selecting a particular node of the pluralityof nodes and indicating an action to perform, the action related to aparticular computing resource represented by the selected particularnode.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input selecting a particular node of the pluralityof nodes and indicating an action to perform, the action related to aparticular computing resource represented by the selected particularnode; sending a request to at least one of the one or more cloudcomputing services to perform the action related to the particularcomputing resource.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input selecting a particular node of the pluralityof nodes and indicating an action to perform related to a particularcomputing resource represented by the selected particular node; sendinga request to the cloud computing service to perform the action relatedto the particular computing resource; wherein the action is one or moreof: creating a new computing resource, modifying an existing computingresource, deleting an existing computing resource, creating a trigger,and creating a time-based job.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input selecting a first particular node and asecond particular node of the plurality of nodes and indicating arelationship to create between a first computing resource represented bythe selected first particular node and a second computing resourcerepresented by the selected second particular node; sending a request tothe cloud computing service to create the indicated relationship betweenthe first computing resource and the second computing resource.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input selecting at least two nodes of the pluralityof nodes; causing display of a menu including one or more selectableactions, each of the one or more selectable actions relevant to theselected at least two nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input indicating dragging a particular first nodeof the plurality of nodes from a first location on the graphical userinterface to a second location on the graphical user interface, thesecond location associated with one or more particular second nodes; inresponse to receiving the input, creating a new relationship between theparticular first node and the one or more particular second nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the one or more cloud computing services includes atleast two cloud computing services, wherein each cloud computing serviceof the at least two cloud computing services is offered by a separatecloud computing service provider.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the plurality of computing resources managed by theone or more cloud computing services includes at least one firstcomputing resource associated with a first region specified by aparticular cloud computing service and at least one second computingresource associated with a second region specified by the particularcloud computing service.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the plurality of computing resources managed by theone or more cloud computing services includes at least one firstcomputing resource associated with a first user account of a particularcloud computing service and at least one second computing resourceassociated with a second user account of the particular cloud computingservice.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input specifying a first point in time and a secondpoint in time, the first point in time associated with a first topologymap and the second point in time associated with a second topology map;displaying a comparison topology map, the comparison topology mapincluding a graphical display indicating one or more differences betweenthe first topology map and the second topology map.

In an embodiment, a method or non-transitory computer readable mediumcomprises: storing the performance data in a data intake and querysystem as a plurality of events.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input selecting one or more nodes of the pluralityof nodes; in response to receiving the input, causing display of one ormore events associated with one or more computing resources representedby the selected one or more nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: causing display of a second graphical user interfacedisplaying a plurality of events, wherein at least one of the events isrelated to one or more computing resources of the plurality of computingresources; receiving input selecting the one or more computing resourcesrelated to the at least one of the events; in response to receiving theinput, causing display of a graphical user interface including atopology map, the topology map including one or more nodes representingthe selected one or more computing resources.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input selecting one or more nodes of the pluralityof nodes; in response to receiving the input, causing display ofperformance data associated with one or more computing resourcesrepresented by the one or more nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input selecting one or more nodes of the pluralityof nodes; in response to receiving the input, causing display of one ormore metrics generated based on performance information associated withone or more computing resources represented by the selected one or morenodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input selecting one or more nodes of the pluralityof nodes; in response to receiving the input, causing display of costinformation associated with one or more computing resources representedby the selected one or more nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input selecting at least two nodes of the pluralityof nodes; in response to receiving the input, causing display ofaggregate information associated with at least two computing resourcesrepresented by the selected at least two nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one node of the plurality of nodes isdisplayed using a particular graphical element based on cost dataassociated with the at least one node.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the performance data includes one or more of: statedata, activity log data, and performance log data.

In an embodiment, a method or non-transitory computer readable mediumcomprises: retrieving state data related to a state of a plurality ofcloud computing resources at each of a plurality of points in time, thestate data including information related to one or more of:relationships among the plurality of cloud computing resources, andperformance information related to one or more cloud computing resourcesof the plurality of cloud computing resources; generating, for each ofthe plurality of points in time, topology map data based on the statedata for the point in time, the topology map data providing instructionsfor displaying the one or more cloud computing resources as a topologymap; causing display of an animated topology map on a graphical userinterface, the animated topology map displaying a series of topologymaps generated based on the topology map data for each of the pluralityof points in time.

In an embodiment, a method or non-transitory computer readable mediumcomprises: causing display of one or more performance metrics related toone or more of the plurality of cloud computing resources for each ofthe plurality of points in time; wherein causing display of theperformance metrics includes synchronizing the display of theperformance metrics with the display of the animated topology map.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources, and oneor more edges representing relationships among the plurality of cloudcomputing resources.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the state data includes both performance data relatedto performance of a plurality of cloud computing resources managed byone or more cloud computing services, and relationship data related torelationships among the plurality of cloud computing resources managedby the one or more cloud computing services.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the state data includes both performance data relatedto performance of a plurality of cloud computing resources managed byone or more cloud computing services, and relationship data related torelationships among the plurality of cloud computing resources managedby the one or more cloud computing services; and wherein at least aportion of the performance data is received from the one or more cloudcomputing services.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the state data includes both performance data relatedto performance of a plurality of cloud computing resources managed byone or more cloud computing services, and relationship data related torelationships among the plurality of cloud computing resources managedby the one or more cloud computing services; and wherein at least aportion of the relationship data is received from the one or more cloudcomputing services.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one cloud computing resource of theplurality of cloud computing resources is one of: a virtual privatecloud, a server instance, a subnet, a storage volume, a networkinterface, an access control list (ACL), a route table, and an internetgateway.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one relationship between a first computingresource and a second computing resource is one of: the first computingresource is a member of a subnet represented by the second computingresource, the first computing resource is a member of a virtual privatecloud represented by the second computing resource, the first computingresource is a storage volume associated with the second computingresource, and the first computing resource is a member of a securitygroup represented by the second computing resource.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources, and oneor more edges representing relationships among the plurality of cloudcomputing resources; wherein at least one node of the plurality of nodesis displayed using a particular graphical element based on performancedata associated with the computing resource represented by the at leastone node.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources, and oneor more edges representing relationships among the plurality of cloudcomputing resources; wherein at least one edge of the one or more edgesis displayed using a particular graphical element based on performancedata associated with a network connection between the cloud computingresources represented by the nodes connected by the at least one edge.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources, and oneor more edges representing relationships among the plurality of cloudcomputing resources; wherein at least one edge of the one or more edgesis displayed using a particular graphical element based on relationshipinformation associated with the computing resources represented by thenodes connected by the at least one edge.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources; receivinginput selecting one or more nodes of the plurality of nodes andindicating an action to perform, the action related to one or morecomputing resources represented by the selected one or more nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources; receivinginput selecting one or more nodes of the plurality of nodes andindicating an action to perform, the action related to one or more cloudcomputing resources represented by the selected one or more nodes;sending a request to at least one of the one or more cloud computingservices to perform the action related to the selected one or morenodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources; receivinginput selecting one or more nodes of the plurality of nodes andindicating an action to perform, the action related to one or more cloudcomputing resources represented by the selected one or more nodes;sending a request to at least one of the one or more cloud computingservices to perform the action related to the one or more cloudcomputing resources; wherein the action is one or more of: creating anew computing resource, modifying an existing computing resource,deleting an existing computing resource, creating a trigger, andcreating a time-based job.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources; receivinginput selecting a first node and a second node of the plurality of nodesand indicating a relationship to create between a first computingresource represented by the selected first node and a second computingresource represented by the selected second node; sending a request tothe cloud computing service to create the indicated relationship betweenthe first computing resource and the second computing resource.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources; receivinginput selecting at least two nodes of the plurality of nodes; causingdisplay of a menu including one or more selectable actions, each of theone or more selectable actions relevant to the selected at least twonodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources; receivinginput indicating dragging a first node of the plurality of nodes from afirst location on the graphical user interface to a second location onthe graphical user interface, the second location associated with one ormore second nodes; in response to receiving the input, creating a newrelationship between the first node and the one or more second nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the one or more cloud computing services includes atleast two cloud computing services, wherein each cloud computing serviceof the at least two cloud computing services is offered by a separatecloud computing service provider.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the plurality of cloud computing resources includesat least one first cloud computing resource associated with a firstregion specified by a particular cloud computing service and at leastone second cloud computing resource associated with a second regionspecified by the particular cloud computing service.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the plurality of cloud computing resources includesat least one first computing resource associated with a first useraccount of a particular cloud computing service and at least one secondcomputing resource associated with a second user account of theparticular cloud computing service.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input specifying a first point in time and a secondpoint in time, the first point in time associated with a first topologymap and the second point in time associated with a second topology map;displaying a comparison topology map, the comparison topology mapincluding a graphical display indicating one or more differences betweenthe first topology map and the second topology map.

In an embodiment, a method or non-transitory computer readable mediumcomprises: storing the performance data in a data intake and querysystem as a plurality of events.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources; receivinginput selecting one or more nodes of the plurality of nodes; in responseto receiving the input, causing display of one or more stored eventsassociated with one or more cloud computing resources represented by theselected one or more nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: causing display of a second graphical user interfacedisplaying a plurality of events, at least one of the events related toone or more cloud computing resources of the plurality of computingresources; receiving input selecting the one or more computing resource;in response to receiving the input, causing display of a graphical userinterface including a topology map, the topology map including one ormore nodes representing the selected one or more cloud computingresources.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources; receivinginput selecting one or more nodes of the plurality of nodes; in responseto receiving the input, causing display of performance data associatedwith one or more cloud computing resources represented by the selectedone or more nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources; receivinginput selecting at least two nodes of the plurality of nodes; inresponse to receiving the input, causing display of aggregateinformation associated with at least two cloud computing resourcesrepresented by the selected at least two nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the animated topology map includes a plurality ofnodes representing the plurality of cloud computing resources, and oneor more edges representing relationships among the plurality of cloudcomputing resources; and wherein at least one node of the plurality ofnodes is displayed using a particular graphical element based on costdata associated with the at least one node.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the series of topology maps are displayed in achronological order.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving, by a controller node, data collection performanceinformation from a data collection node of a plurality of datacollection nodes, the data collection performance information related todata collection requests sent by the data collection node to a cloudcomputing service provider; generating, based on the data collectionperformance information, instructions for the data collection node tomodify a rate at which the data collection node sends data collectionrequests to the cloud computing service provider; sending theinstructions to the data collection node.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data collection requests are sent to anapplication programming interface (API) associated with the cloudcomputing service provider.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the instructions instruct the data collection node toincrease the rate at which the data collection node sends the datacollection requests to the cloud computing service provider.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the instructions instruct the data collection node todecrease the rate at which the data collection node sends the datacollection requests to the cloud computing service provider.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data collection performance information includesinformation indicating one or more error messages received by the datacollection node from the cloud computing service provider.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data collection performance information includesnetwork traffic data.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data collection performance information includesCPU utilization information for the data collection node.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data collection performance information includesinformation indicating one or more attributes of the cloud computingservice provider.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data collection performance information includesinformation indicating one or more data collection policies of the cloudcomputing service provider.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the controller node receives data collectionperformance information from each data collection node of the pluralityof data collection nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the controller node receives data collectionperformance information from each data collection node of the pluralityof data collection nodes, and wherein the plurality of data collectionnodes sends data collection requests to a plurality of cloud computingservice providers.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data collection node sends data collectionrequests to a plurality of cloud computing service providers.

In an embodiment, a method or non-transitory computer readable mediumcomprises: subsequent to generating the instructions, receivingadditional data collection performance information from the datacollection node; generating, based at least in part on the additionaldata collection performance information, updated instructions for thedata collection node to modify the rate at which the data collectionnode sends data collection requests to the cloud computing serviceprovider; sending the updated instructions to the data collection node.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data collection node sends data collectionrequests to a plurality of cloud computing service providers, andwherein the controller node generates and sends to the data collectionnode separate instructions for each different cloud computing serviceprovider of the plurality of cloud computing service providers.

In an embodiment, a method or non-transitory computer readable mediumcomprises: generating, based on the data collection performanceinformation, second instructions for a different data collection node ofthe plurality of data collection nodes to send data collection requeststo the cloud computing service provider; sending the second instructionsto the different data collection node.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data collection requests sent to the cloudcomputing service provider request data related to performance and/orsecurity information for one or more computing resources managed by thecloud computing service provider.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein data collected from the cloud computing serviceproviders is sent to one or more indexers to be parsed and stored asevent data reflecting events of one or more resources of an informationtechnology environment.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein data collected from the cloud computing serviceproviders comprises raw data sent to one or more indexers, and whereinthe one or more indexers parse a plurality of timestamped events fromthe raw data, and wherein each timestamped event of the plurality oftimestamped events includes a portion of the raw data.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving, by the data collection node, the instructions;sending, by the data collection node, data collection requests to thecloud computing service provider at a rate based on the instructions.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving, by the controller node, an indication that newdata collection node has joined the plurality of data collection nodes;modifying instructions for one or more data collection nodes of theplurality based on the indication that the new data collection node hasjoined the plurality of data collection nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving, by the controller node, an indication that datacollection node has left the plurality of data collection nodes;modifying instructions for one or more data collection nodes of theplurality based on the indication that a data collection node has leftthe plurality of data collection nodes.

In an embodiment, a method or non-transitory computer readable mediumcomprises: determining that the data collection performance informationindicates that the cloud computing service provider has upgraded itsdata collection capabilities; in response to determining that the cloudcomputing service provider has upgraded its data collectioncapabilities, instructing the data collection node to increase the rateat which the data collection node sends the data collection requests tothe cloud computing service provider.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data collection performance information indicatesthat the cloud computing service provider has upgraded its datacollection capabilities based on one or more of: one or more statusmessages, one or more version identifiers, an operational pattern ofcloud computing service provider resources, and data retrieved from awebsite separate from the cloud computing service provider; in responseto determining that the data collection performance information includesinformation indicating an upgrade to the cloud computing serviceprovider, instructing the data collection node to increase the rate atwhich the data collection node sends the data collection requests to thecloud computing service provider.

Other examples of these and other embodiments are found throughout thisdisclosure.

6.0. IMPLEMENTATION MECHANISM Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be desktop computer systems,portable computer systems, handheld devices, networking devices or anyother device that incorporates hard-wired and/or program logic toimplement the techniques. The special-purpose computing devices may behard-wired to perform the techniques, or may include digital electronicdevices such as one or more application-specific integrated circuits(ASICs) or field programmable gate arrays (FPGAs) that are persistentlyprogrammed to perform the techniques, or may include one or more generalpurpose hardware processors programmed to perform the techniquespursuant to program instructions in firmware, memory, other storage, ora combination thereof. Such special-purpose computing devices may alsocombine custom hard-wired logic, ASICs, or FPGAs with custom programmingto accomplish the techniques.

FIG. 35 is a block diagram that illustrates a computer system 3500utilized in implementing the above-described techniques, according to anembodiment. Computer system 3500 may be, for example, a desktopcomputing device, laptop computing device, tablet, smartphone, serverappliance, computing mainframe, multimedia device, handheld device,networking apparatus, or any other suitable device.

Computer system 3500 includes one or more busses 3502 or othercommunication mechanism for communicating information, and one or morehardware processors 3504 coupled with busses 3502 for processinginformation. Hardware processors 3504 may be, for example, generalpurpose microprocessors. Busses 3502 may include various internal and/orexternal components, including, without limitation, internal processoror memory busses, a Serial ATA bus, a PCI Express bus, a UniversalSerial Bus, a HyperTransport bus, an Infiniband bus, and/or any othersuitable wired or wireless communication channel.

Computer system 3500 also includes a main memory 3506, such as a randomaccess memory (RAM) or other dynamic or volatile storage device, coupledto bus 3502 for storing information and instructions to be executed byprocessor 3504. Main memory 3506 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 3504. Such instructions, whenstored in non-transitory storage media accessible to processor 3504,render computer system 3500 a special-purpose machine that is customizedto perform the operations specified in the instructions.

Computer system 3500 further includes one or more read only memories(ROM) 3508 or other static storage devices coupled to bus 3502 forstoring static information and instructions for processor 3504. One ormore storage devices 3510, such as a solid-state drive (SSD), magneticdisk, optical disk, or other suitable non-volatile storage device, isprovided and coupled to bus 3502 for storing information andinstructions.

Computer system 3500 may be coupled via bus 3502 to one or more displays3512 for presenting information to a computer user. For instance,computer system 3500 may be connected via an High-Definition MultimediaInterface (HDMI) cable or other suitable cabling to a Liquid CrystalDisplay (LCD) monitor, and/or via a wireless connection such aspeer-to-peer Wi-Fi Direct connection to a Light-Emitting Diode (LED)television. Other examples of suitable types of displays 3512 mayinclude, without limitation, plasma display devices, projectors, cathoderay tube (CRT) monitors, electronic paper, virtual reality headsets,braille terminal, and/or any other suitable device for outputtinginformation to a computer user. In an embodiment, any suitable type ofoutput device, such as, for instance, an audio speaker or printer, maybe utilized instead of a display 3512.

One or more input devices 3514 are coupled to bus 3502 for communicatinginformation and command selections to processor 3504. One example of aninput device 3514 is a keyboard, including alphanumeric and other keys.Another type of user input device 3514 is cursor control 3516, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 3504 and for controllingcursor movement on display 3512. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Yetother examples of suitable input devices 3514 include a touch-screenpanel affixed to a display 3512, cameras, microphones, accelerometers,motion detectors, and/or other sensors. In an embodiment, anetwork-based input device 3514 may be utilized. In such an embodiment,user input and/or other information or commands may be relayed viarouters and/or switches on a Local Area Network (LAN) or other suitableshared network, or via a peer-to-peer network, from the input device3514 to a network link 3520 on the computer system 3500.

A computer system 3500 may implement techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 3500 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 3500 in response to processor 3504 executing one or moresequences of one or more instructions contained in main memory 3506.Such instructions may be read into main memory 3506 from another storagemedium, such as storage device 3510. Execution of the sequences ofinstructions contained in main memory 3506 causes processor 3504 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 3510.Volatile media includes dynamic memory, such as main memory 3506. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, an EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 3502. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 3504 for execution. Forexample, the instructions may initially be carried on a magnetic disk ora solid state drive of a remote computer. The remote computer can loadthe instructions into its dynamic memory and use a modem to send theinstructions over a network, such as a cable network or cellularnetwork, as modulate signals. A modem local to computer system 3500 canreceive the data on the network and demodulate the signal to decode thetransmitted instructions. Appropriate circuitry can then place the dataon bus 3502. Bus 3502 carries the data to main memory 3506, from whichprocessor 3504 retrieves and executes the instructions. The instructionsreceived by main memory 3506 may optionally be stored on storage device3510 either before or after execution by processor 3504.

A computer system 3500 may also include, in an embodiment, one or morecommunication interfaces 3518 coupled to bus 3502. A communicationinterface 3518 provides a data communication coupling, typicallytwo-way, to a network link 3520 that is connected to a local network3522. For example, a communication interface 3518 may be an integratedservices digital network (ISDN) card, cable modem, satellite modem, or amodem to provide a data communication connection to a corresponding typeof telephone line. As another example, the one or more communicationinterfaces 3518 may include a local area network (LAN) card to provide adata communication connection to a compatible LAN. As yet anotherexample, the one or more communication interfaces 3518 may include awireless network interface controller, such as a 802.11-basedcontroller, Bluetooth controller, Long Term Evolution (LTE) modem,and/or other types of wireless interfaces. In any such implementation,communication interface 3518 sends and receives electrical,electromagnetic, or optical signals that carry digital data streamsrepresenting various types of information.

Network link 3520 typically provides data communication through one ormore networks to other data devices. For example, network link 3520 mayprovide a connection through local network 3522 to a host computer 3524or to data equipment operated by a Service Provider 3526. ServiceProvider 3526, which may for example be an Internet Service Provider(ISP), in turn provides data communication services through a wide areanetwork, such as the world wide packet data communication network nowcommonly referred to as the “Internet” 3528. Local network 3522 andInternet 3528 both use electrical, electromagnetic or optical signalsthat carry digital data streams. The signals through the variousnetworks and the signals on network link 3520 and through communicationinterface 3518, which carry the digital data to and from computer system3500, are example forms of transmission media.

In an embodiment, computer system 3500 can send messages and receivedata, including program code and/or other types of instructions, throughthe network(s), network link 3520, and communication interface 3518. Inthe Internet example, a server 3530 might transmit a requested code foran application program through Internet 3528, ISP 3526, local network3522 and communication interface 3518. The received code may be executedby processor 3504 as it is received, and/or stored in storage device3510, or other non-volatile storage for later execution. As anotherexample, information received via a network link 3520 may be interpretedand/or processed by a software component of the computer system 3500,such as a web browser, application, or server, which in turn issuesinstructions based thereon to a processor 3504, possibly via anoperating system and/or other intermediate layers of softwarecomponents.

In an embodiment, some or all of the systems described herein may be orcomprise server computer systems, including one or more computer systems3500 that collectively implement various components of the system as aset of server-side processes. The server computer systems may includeweb server, application server, database server, and/or otherconventional server components that certain above-described componentsutilize to provide the described functionality. The server computersystems may receive network-based communications comprising input datafrom any of a variety of sources, including without limitationuser-operated client computing devices such as desktop computers,tablets, or smartphones, remote sensing devices, and/or other servercomputer systems.

In an embodiment, certain server components may be implemented in fullor in part using “cloud”-based components that are coupled to thesystems by one or more networks, such as the Internet. The cloud-basedcomponents may expose interfaces by which they provide processing,storage, software, and/or other resources to other components of thesystems. In an embodiment, the cloud-based components may be implementedby third-party entities, on behalf of another entity for whom thecomponents are deployed. In other embodiments, however, the describedsystems may be implemented entirely by computer systems owned andoperated by a single entity.

In an embodiment, an apparatus comprises a processor and is configuredto perform any of the foregoing methods. In an embodiment, anon-transitory computer readable storage medium, storing softwareinstructions, which when executed by one or more processors causeperformance of any of the foregoing methods.

7.0. EXTENSIONS AND ALTERNATIVES

As used herein, the terms “first,” “second,” “certain,” and “particular”are used as naming conventions to distinguish queries, plans,representations, steps, objects, devices, or other items from eachother, so that these items may be referenced after they have beenintroduced. Unless otherwise specified herein, the use of these termsdoes not imply an ordering, timing, or any other characteristic of thereferenced items.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. Thus, the sole and exclusive indicatorof what is the invention, and is intended by the applicants to be theinvention, is the set of claims that issue from this application, in thespecific form in which such claims issue, including any subsequentcorrection. In this regard, although specific claim dependencies are setout in the claims of this application, it is to be noted that thefeatures of the dependent claims of this application may be combined asappropriate with the features of other dependent claims and with thefeatures of the independent claims of this application, and not merelyaccording to the specific dependencies recited in the set of claims.Moreover, although separate embodiments are discussed herein, anycombination of embodiments and/or partial embodiments discussed hereinmay be combined to form further embodiments.

Any definitions expressly set forth herein for terms contained in suchclaims shall govern the meaning of such terms as used in the claims.Hence, no limitation, element, property, feature, advantage or attributethat is not expressly recited in a claim should limit the scope of suchclaim in any way. The specification and drawings are, accordingly, to beregarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A method, comprising: receiving, by a controllernode, data collection performance information from a data collectionnode of a plurality of data collection nodes, the data collectionperformance information related to data collection requests sent by thedata collection node to a cloud computing service provider; generating,based on the data collection performance information, instructions forthe data collection node to modify a rate at which the data collectionnode sends data collection requests to the cloud computing serviceprovider; sending the instructions to the data collection node.
 2. Themethod of claim 1, wherein the data collection requests are sent to anapplication programming interface (API) associated with the cloudcomputing service provider.
 3. The method of claim 1, wherein theinstructions instruct the data collection node to increase the rate atwhich the data collection node sends the data collection requests to thecloud computing service provider.
 4. The method of claim 1, wherein theinstructions instruct the data collection node to decrease the rate atwhich the data collection node sends the data collection requests to thecloud computing service provider.
 5. The method of claim 1, wherein thedata collection performance information includes information indicatingone or more error messages received by the data collection node from thecloud computing service provider.
 6. The method of claim 1, wherein thedata collection performance information includes network traffic data.7. The method of claim 1, wherein the data collection performanceinformation includes CPU utilization information for the data collectionnode.
 8. The method of claim 1, wherein the data collection performanceinformation includes information indicating one or more attributes ofthe cloud computing service provider.
 9. The method of claim 1, whereinthe data collection performance information includes informationindicating one or more data collection policies of the cloud computingservice provider.
 10. The method of claim 1, wherein the controller nodereceives data collection performance information from each datacollection node of the plurality of data collection nodes.
 11. Themethod of claim 1, wherein the controller node receives data collectionperformance information from each data collection node of the pluralityof data collection nodes, and wherein the plurality of data collectionnodes sends data collection requests to a plurality of cloud computingservice providers.
 12. The method of claim 1, wherein the datacollection node sends data collection requests to a plurality of cloudcomputing service providers.
 13. The method of claim 1, furthercomprising: subsequent to generating the instructions, receivingadditional data collection performance information from the datacollection node; generating, based at least in part on the additionaldata collection performance information, updated instructions for thedata collection node to modify the rate at which the data collectionnode sends data collection requests to the cloud computing serviceprovider; sending the updated instructions to the data collection node.14. The method of claim 1, wherein the data collection node sends datacollection requests to a plurality of cloud computing service providers,and wherein the controller node generates and sends to the datacollection node separate instructions for each different cloud computingservice provider of the plurality of cloud computing service providers.15. The method of claim 1, further comprising: generating, based on thedata collection performance information, second instructions for adifferent data collection node of the plurality of data collection nodesto send data collection requests to the cloud computing serviceprovider; sending the second instructions to the different datacollection node.
 16. The method of claim 1, wherein the data collectionrequests sent to the cloud computing service provider request datarelated to performance and/or security information for one or morecomputing resources managed by the cloud computing service provider. 17.The method of claim 1, wherein data collected from the cloud computingservice providers is sent to one or more indexers to be parsed andstored as event data reflecting events of one or more resources of aninformation technology environment.
 18. The method of claim 1, whereindata collected from the cloud computing service providers comprises rawdata sent to one or more indexers, and wherein the one or more indexersparse a plurality of timestamped events from the raw data, and whereineach timestamped event of the plurality of timestamped events includes aportion of the raw data.
 19. The method of claim 1, further comprising:receiving, by the data collection node, the instructions; sending, bythe data collection node, data collection requests to the cloudcomputing service provider at a rate based on the instructions.
 20. Themethod of claim 1, further comprising: receiving, by the controllernode, an indication that new data collection node has joined theplurality of data collection nodes; modifying instructions for one ormore data collection nodes of the plurality based on the indication thatthe new data collection node has joined the plurality of data collectionnodes.
 21. The method of claim 1, further comprising: receiving, by thecontroller node, an indication that data collection node has left theplurality of data collection nodes; modifying instructions for one ormore data collection nodes of the plurality based on the indication thata data collection node has left the plurality of data collection nodes.22. The method of claim 1, further comprising: determining that the datacollection performance information indicates that the cloud computingservice provider has upgraded its data collection capabilities; inresponse to determining that the cloud computing service provider hasupgraded its data collection capabilities, instructing the datacollection node to increase the rate at which the data collection nodesends the data collection requests to the cloud computing serviceprovider.
 23. The method of claim 1, further comprising: wherein thedata collection performance information indicates that the cloudcomputing service provider has upgraded its data collection capabilitiesbased on one or more of: one or more status messages, one or moreversion identifiers, an operational pattern of cloud computing serviceprovider resources, and data retrieved from a website separate from thecloud computing service provider; in response to determining that thedata collection performance information includes information indicatingan upgrade to the cloud computing service provider, instructing the datacollection node to increase the rate at which the data collection nodesends the data collection requests to the cloud computing serviceprovider.
 24. One or more non-transitory storage media storinginstructions which, when executed by one or more computing devices,cause: receiving, by a controller node, data collection performanceinformation from a data collection node of a plurality of datacollection nodes, the data collection performance information related todata collection requests sent by the data collection node to a cloudcomputing service provider; generating, based on the data collectionperformance information, instructions for the data collection node tomodify a rate at which the data collection node sends data collectionrequests to the cloud computing service provider; sending theinstructions to the data collection node.
 25. An apparatus, comprising:a subsystem, implemented at least partially in hardware, that receives,by a controller node, data collection performance information from adata collection node of a plurality of data collection nodes, the datacollection performance information related to data collection requestssent by the data collection node to a cloud computing service provider;a subsystem, implemented at least partially in hardware, that generates,based on the data collection performance information, instructions forthe data collection node to modify a rate at which the data collectionnode sends data collection requests to the cloud computing serviceprovider; a subsystem, implemented at least partially in hardware, thatsends the instructions to the data collection node.